Unanswered Questions
72,668 questions with no upvoted or accepted answers
12
votes
0
answers
431
views
Official name of a common type of Bayesian simulation study
There is a kind of simulation study that is commonly used to validate an implementation of a Bayesian model:
For independent replication $i = 1, ..., n$:
Draw a set of "true" parameters ...
- 267
12
votes
0
answers
2k
views
Instrumental variables with interactions between endogenous variables
I have two endogenous variables $x_1$ and $x_2$ and am trying to estimate the following model:
$$y = \theta_0 + \theta_1 x_1 + \theta_2 x_2 + \theta_{12} x_{12}$$
where $x_{12} = x_1\times x_2$. I'm ...
- 5,559
12
votes
0
answers
975
views
Why we really need the concept of "Local" Rademacher complexity?
Recently, I have been studying High-Dimensional Statistics: A Non-Asymptotic Viewpoint written by Martin J. Wainwright. In this book, the author uses a special complexity measure which is called Local ...
- 348
12
votes
1
answer
350
views
Correlation between two binary variables within one categorical variable
The Problem:
I have measured two binary variables within 1 categorical variable with 5 levels.
Initially, I thought I'd be able to use Fisher's Exact test or some $N \times M \times K$ version of it. ...
- 81.5k
12
votes
0
answers
2k
views
Is sparsity of topics a necessary condition for latent Dirichlet allocation (LDA) to work
I have been playing with the hyper-parameters of the latent Dirichlet allocation (LDA) model and am wondering how sparsity of topic priors play a role in inference.
I have not performed these ...
CommunityBot
- 1
12
votes
0
answers
769
views
What approaches use multiple eigenvectors in graph spectral clustering?
Background: In Newman's PNAS 2006 paper Modularity and community structure in networks, the first eigenvector splits the graph in two clusters, and then each cluster can be further divided by ...
- 6,379
12
votes
0
answers
2k
views
Empirical Prediction interval for time series forecast based on quantile regression
As Gardner notes "almost all point forecasts are wrong", so prediction intervals (PI) are necessary to quantify uncertainty and help us make informed decisions. There exists theoretical PI, and in ...
- 8,485
12
votes
0
answers
5k
views
How to use formative indicators in covariance-based SEM with lavaan?
I'm trying to build a covariance-based structural equation model (SEM) using both reflective and formative specifications of latent variables. I use the sem ...
- 2,854
12
votes
0
answers
110
views
Words that estimate numerical proportions of a group
There seem to be more than a few papers that surround the use of certain words to define the probability of an event without actually using the numerical probability itself.
For example, if you are ...
- 58.4k
12
votes
0
answers
14k
views
How to normalize data prior to computation of covariance matrix
In all my self-study, I have come across many different ways in which people seem to normalize their data, prior to the computation of the covariance matrix. I am confused as to what ways are 'correct'...
- 329k
12
votes
0
answers
1k
views
Computing a bootstrap confidence interval for the prediction error with the percentile and the BCa method
I have two related questions regarding the computation of a non-parametric bootstrap confidence interval for the prediction error.
Setting: I have a sample S from a data population P and a learner L, ...
CommunityBot
- 1
11
votes
0
answers
1k
views
Bootstrap Prediction Interval: which residuals to use and which method?
I ask this question referring to the post: Bootstrap prediction interval, where a step by step method for calculating the prediction interval for linear regression models is explained.
In the ...
- 311
11
votes
1
answer
840
views
Proof that the addition of a baseline to the REINFORCE algorithm reduces the variance
A widely used variation of REINFORCE is to subtract a baseline value $b$ from the return $G_t$ to reduce the variance of gradient estimation, such that
\begin{align}
\nabla_\theta J(\theta) & \...
CommunityBot
- 1
11
votes
1
answer
3k
views
Feature selection using chi squared for continuous features
I'm looking at univariate feature selection. A method that is often described, is to look at the p-values for a $\chi^2$-test. However, I'm confused as to how this works for continuous variables.
1. ...
CommunityBot
- 1
11
votes
3
answers
2k
views
Need advice on change point (step) detection
I have a time series with lots of steps/jumps (data file here). A plot is given below. I would like to subtract an appropriate value for each of these square wave features to bring them back down to ...
CommunityBot
- 1